diff --git a/README.md b/README.md index c997453..dc79485 100644 --- a/README.md +++ b/README.md @@ -33,6 +33,8 @@ python -m pip install tclf - Quote rule[^4] - Tradesize rule[^3] +For a primer on trade classification rules visit the [rules section 🆕](https://karelze.github.io/tclf/rules/) in our docs. + ## Minimal Example Let's start simple: classify all trades by the quote rule and all other trades, which cannot be classified by the quote rule, randomly. @@ -98,7 +100,21 @@ acc = accuracy_score(y_true, clf.predict(X)) ``` In this example, input data is available as np.arrays with both exchange (`"ex"`) and nbbo data (`"best"`). We set the layers parameter to `layers=[("quote", "ex"), ("quote", "best")]` to classify trades first on subset `"ex"` and remaining trades on subset `"best"`. Additionally, we have to set `ClassicalClassifier(..., features=features)` to pass column information to the classifier. -Like before, column/feature names must follow our [naming conventions](https://karelze.github.io/tclf/naming_conventions/). For more practical examples, see our [examples section](https://karelze.github.io/tclf/option_trade_classification). +Like before, column/feature names must follow our [naming conventions](https://karelze.github.io/tclf/naming_conventions/). + +## Other Examples + +For more practical examples, see our [examples section](https://karelze.github.io/tclf/option_trade_classification). + +## Development + +We are using [`pixi`](https://github.com/prefix-dev/pixi) as a dependency management and workflow tool. + +```bash +pixi install +pixi run postinstall +pixi run test +``` ## Citation @@ -116,16 +132,6 @@ If you are using the package in publications, please cite as: } ``` -## Development - -We are using [`pixi`](https://github.com/prefix-dev/pixi) as a dependency management and workflow tool. - -```bash -pixi install -pixi run postinstall -pixi run test -``` - ## Footnotes [^1]:
Chakrabarty, B., Li, B., Nguyen, V., & Van Ness, R. A. (2007). Trade classification algorithms for electronic communications network trades. Journal of Banking & Finance, 31(12), 3806–3821. https://doi.org/10.1016/j.jbankfin.2007.03.003
diff --git a/docs/index.md b/docs/index.md index c997453..dc79485 100644 --- a/docs/index.md +++ b/docs/index.md @@ -33,6 +33,8 @@ python -m pip install tclf - Quote rule[^4] - Tradesize rule[^3] +For a primer on trade classification rules visit the [rules section 🆕](https://karelze.github.io/tclf/rules/) in our docs. + ## Minimal Example Let's start simple: classify all trades by the quote rule and all other trades, which cannot be classified by the quote rule, randomly. @@ -98,7 +100,21 @@ acc = accuracy_score(y_true, clf.predict(X)) ``` In this example, input data is available as np.arrays with both exchange (`"ex"`) and nbbo data (`"best"`). We set the layers parameter to `layers=[("quote", "ex"), ("quote", "best")]` to classify trades first on subset `"ex"` and remaining trades on subset `"best"`. Additionally, we have to set `ClassicalClassifier(..., features=features)` to pass column information to the classifier. -Like before, column/feature names must follow our [naming conventions](https://karelze.github.io/tclf/naming_conventions/). For more practical examples, see our [examples section](https://karelze.github.io/tclf/option_trade_classification). +Like before, column/feature names must follow our [naming conventions](https://karelze.github.io/tclf/naming_conventions/). + +## Other Examples + +For more practical examples, see our [examples section](https://karelze.github.io/tclf/option_trade_classification). + +## Development + +We are using [`pixi`](https://github.com/prefix-dev/pixi) as a dependency management and workflow tool. + +```bash +pixi install +pixi run postinstall +pixi run test +``` ## Citation @@ -116,16 +132,6 @@ If you are using the package in publications, please cite as: } ``` -## Development - -We are using [`pixi`](https://github.com/prefix-dev/pixi) as a dependency management and workflow tool. - -```bash -pixi install -pixi run postinstall -pixi run test -``` - ## Footnotes [^1]:
Chakrabarty, B., Li, B., Nguyen, V., & Van Ness, R. A. (2007). Trade classification algorithms for electronic communications network trades. Journal of Banking & Finance, 31(12), 3806–3821. https://doi.org/10.1016/j.jbankfin.2007.03.003